Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations1495
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory344.3 KiB
Average record size in memory235.8 B

Variable types

Numeric9
Text2
Categorical1

Alerts

Cost of Living Index is highly overall correlated with Health Care Index and 4 other fieldsHigh correlation
Health Care Index is highly overall correlated with Cost of Living Index and 3 other fieldsHigh correlation
Pollution Index is highly overall correlated with Cost of Living Index and 4 other fieldsHigh correlation
Property Price to Income Ratio is highly overall correlated with Purchasing Power Index and 2 other fieldsHigh correlation
Purchasing Power Index is highly overall correlated with Cost of Living Index and 5 other fieldsHigh correlation
Quality of Life Index is highly overall correlated with Cost of Living Index and 7 other fieldsHigh correlation
Rank is highly overall correlated with Cost of Living Index and 7 other fieldsHigh correlation
Safety Index is highly overall correlated with Quality of Life Index and 1 other fieldsHigh correlation
Traffic Commute Time Index is highly overall correlated with Pollution Index and 2 other fieldsHigh correlation

Reproduction

Analysis started2025-03-06 00:01:46.466756
Analysis finished2025-03-06 00:01:55.404324
Duration8.94 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Rank
Real number (ℝ)

High correlation 

Distinct87
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.733779
Minimum1
Maximum87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2025-03-06T02:01:55.520813image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q119
median38
Q357
95-th percentile78
Maximum87
Range86
Interquartile range (IQR)38

Descriptive statistics

Standard deviation22.931071
Coefficient of variation (CV)0.5920174
Kurtosis-1.0309912
Mean38.733779
Median Absolute Deviation (MAD)19
Skewness0.16123725
Sum57907
Variance525.83403
MonotonicityNot monotonic
2025-03-06T02:01:55.628350image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 20
 
1.3%
30 20
 
1.3%
32 20
 
1.3%
33 20
 
1.3%
34 20
 
1.3%
35 20
 
1.3%
36 20
 
1.3%
37 20
 
1.3%
38 20
 
1.3%
39 20
 
1.3%
Other values (77) 1295
86.6%
ValueCountFrequency (%)
1 20
1.3%
2 20
1.3%
3 20
1.3%
4 20
1.3%
5 20
1.3%
6 20
1.3%
7 20
1.3%
8 20
1.3%
9 20
1.3%
10 20
1.3%
ValueCountFrequency (%)
87 2
 
0.1%
86 3
 
0.2%
85 4
 
0.3%
84 6
0.4%
83 9
0.6%
82 10
0.7%
81 10
0.7%
80 11
0.7%
79 11
0.7%
78 11
0.7%
Distinct98
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size83.4 KiB
2025-03-06T02:01:55.987711image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length22
Median length18
Mean length8.032107
Min length4

Characters and Unicode

Total characters12008
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.6%

Sample

1st rowSwitzerland
2nd rowGermany
3rd rowSweden
4th rowUnited States
5th rowFinland
ValueCountFrequency (%)
united 60
 
3.4%
south 40
 
2.2%
china 25
 
1.4%
republic 21
 
1.2%
japan 20
 
1.1%
ireland 20
 
1.1%
denmark 20
 
1.1%
austria 20
 
1.1%
australia 20
 
1.1%
canada 20
 
1.1%
Other values (99) 1514
85.1%
2025-03-06T02:01:56.477209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1753
14.6%
i 1018
 
8.5%
n 1009
 
8.4%
e 901
 
7.5%
r 748
 
6.2%
o 565
 
4.7%
t 501
 
4.2%
l 477
 
4.0%
d 411
 
3.4%
u 380
 
3.2%
Other values (41) 4245
35.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12008
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1753
14.6%
i 1018
 
8.5%
n 1009
 
8.4%
e 901
 
7.5%
r 748
 
6.2%
o 565
 
4.7%
t 501
 
4.2%
l 477
 
4.0%
d 411
 
3.4%
u 380
 
3.2%
Other values (41) 4245
35.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12008
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1753
14.6%
i 1018
 
8.5%
n 1009
 
8.4%
e 901
 
7.5%
r 748
 
6.2%
o 565
 
4.7%
t 501
 
4.2%
l 477
 
4.0%
d 411
 
3.4%
u 380
 
3.2%
Other values (41) 4245
35.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12008
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1753
14.6%
i 1018
 
8.5%
n 1009
 
8.4%
e 901
 
7.5%
r 748
 
6.2%
o 565
 
4.7%
t 501
 
4.2%
l 477
 
4.0%
d 411
 
3.4%
u 380
 
3.2%
Other values (41) 4245
35.4%

Quality of Life Index
Real number (ℝ)

High correlation 

Distinct917
Distinct (%)61.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean134.15666
Minimum-53
Maximum285.8
Zeros1
Zeros (%)0.1%
Negative7
Negative (%)0.5%
Memory size11.8 KiB
2025-03-06T02:01:56.635819image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-53
5-th percentile68.18
Q1105.35
median136.2
Q3165.7
95-th percentile190.83
Maximum285.8
Range338.8
Interquartile range (IQR)60.35

Descriptive statistics

Standard deviation40.828402
Coefficient of variation (CV)0.30433378
Kurtosis0.75511784
Mean134.15666
Median Absolute Deviation (MAD)30.3
Skewness-0.51063488
Sum200564.2
Variance1666.9584
MonotonicityNot monotonic
2025-03-06T02:01:56.744636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
162.3 7
 
0.5%
173.6 6
 
0.4%
134.5 6
 
0.4%
163.5 6
 
0.4%
155.4 5
 
0.3%
100.3 5
 
0.3%
167.5 5
 
0.3%
103.3 5
 
0.3%
156.9 5
 
0.3%
146.1 5
 
0.3%
Other values (907) 1440
96.3%
ValueCountFrequency (%)
-53 1
0.1%
-40.7 1
0.1%
-35.7 1
0.1%
-19.5 1
0.1%
-7.1 1
0.1%
-5.4 1
0.1%
-4.4 1
0.1%
0 1
0.1%
1.1 1
0.1%
3 1
0.1%
ValueCountFrequency (%)
285.8 1
0.1%
254.7 1
0.1%
244.3 1
0.1%
242.7 1
0.1%
237.2 1
0.1%
228.5 1
0.1%
227.2 1
0.1%
223 1
0.1%
222.9 1
0.1%
220.8 1
0.1%

Purchasing Power Index
Real number (ℝ)

High correlation 

Distinct833
Distinct (%)55.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.804615
Minimum3.3
Maximum210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2025-03-06T02:01:56.861160image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum3.3
5-th percentile24.47
Q138.35
median60.7
Q391.8
95-th percentile124.93
Maximum210
Range206.7
Interquartile range (IQR)53.45

Descriptive statistics

Standard deviation33.282943
Coefficient of variation (CV)0.49821323
Kurtosis-0.22624211
Mean66.804615
Median Absolute Deviation (MAD)25.4
Skewness0.57863691
Sum99872.9
Variance1107.7543
MonotonicityNot monotonic
2025-03-06T02:01:56.969520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.3 7
 
0.5%
39.9 7
 
0.5%
40.8 7
 
0.5%
37.2 6
 
0.4%
36.4 6
 
0.4%
32.2 6
 
0.4%
35.5 6
 
0.4%
87.3 6
 
0.4%
33.3 6
 
0.4%
38.6 6
 
0.4%
Other values (823) 1432
95.8%
ValueCountFrequency (%)
3.3 1
0.1%
8.2 1
0.1%
8.4 1
0.1%
8.8 1
0.1%
9.3 1
0.1%
9.4 2
0.1%
9.8 1
0.1%
11 1
0.1%
11.1 1
0.1%
11.3 1
0.1%
ValueCountFrequency (%)
210 1
0.1%
187.4 1
0.1%
182.5 1
0.1%
178.7 1
0.1%
167.9 1
0.1%
164.3 1
0.1%
161.8 1
0.1%
161.4 1
0.1%
161.1 1
0.1%
160.1 1
0.1%

Safety Index
Real number (ℝ)

High correlation 

Distinct501
Distinct (%)33.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.104682
Minimum14.7
Maximum88.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2025-03-06T02:01:57.077830image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum14.7
5-th percentile35.27
Q152.7
median59.9
Q370.7
95-th percentile80.03
Maximum88.1
Range73.4
Interquartile range (IQR)18

Descriptive statistics

Standard deviation13.776743
Coefficient of variation (CV)0.22921248
Kurtosis0.074751361
Mean60.104682
Median Absolute Deviation (MAD)9
Skewness-0.4479316
Sum89856.5
Variance189.79865
MonotonicityNot monotonic
2025-03-06T02:01:57.194307image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59 11
 
0.7%
53.4 10
 
0.7%
53.5 10
 
0.7%
57.8 10
 
0.7%
53.2 10
 
0.7%
60 9
 
0.6%
67.9 9
 
0.6%
57.5 9
 
0.6%
67.4 9
 
0.6%
53.9 9
 
0.6%
Other values (491) 1399
93.6%
ValueCountFrequency (%)
14.7 1
0.1%
14.8 1
0.1%
15.9 1
0.1%
16.4 1
0.1%
16.8 1
0.1%
17.4 1
0.1%
17.9 1
0.1%
18.8 1
0.1%
21.6 2
0.1%
22.1 1
0.1%
ValueCountFrequency (%)
88.1 2
0.1%
88 1
0.1%
87.9 1
0.1%
87.7 1
0.1%
87.3 1
0.1%
86.9 1
0.1%
86.7 1
0.1%
86.3 1
0.1%
86.2 1
0.1%
85.9 1
0.1%

Health Care Index
Real number (ℝ)

High correlation 

Distinct399
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.211037
Minimum28.3
Maximum88.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2025-03-06T02:01:57.302621image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum28.3
5-th percentile47.7
Q157.6
median66.4
Q373.25
95-th percentile80.2
Maximum88.4
Range60.1
Interquartile range (IQR)15.65

Descriptive statistics

Standard deviation10.150324
Coefficient of variation (CV)0.15565347
Kurtosis-0.4490491
Mean65.211037
Median Absolute Deviation (MAD)7.5
Skewness-0.32276271
Sum97490.5
Variance103.02908
MonotonicityNot monotonic
2025-03-06T02:01:57.410981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60.5 12
 
0.8%
52.3 12
 
0.8%
66.3 11
 
0.7%
75.5 11
 
0.7%
60 10
 
0.7%
71.9 10
 
0.7%
69.2 10
 
0.7%
68.2 10
 
0.7%
55.6 9
 
0.6%
58.9 9
 
0.6%
Other values (389) 1391
93.0%
ValueCountFrequency (%)
28.3 1
0.1%
34.3 1
0.1%
35.1 1
0.1%
36.6 1
0.1%
36.9 1
0.1%
38.2 1
0.1%
38.4 1
0.1%
38.5 1
0.1%
38.7 1
0.1%
39.2 2
0.1%
ValueCountFrequency (%)
88.4 1
 
0.1%
87.1 1
 
0.1%
86.9 1
 
0.1%
86.7 1
 
0.1%
86.4 5
0.3%
86.2 1
 
0.1%
86 2
 
0.1%
85.9 2
 
0.1%
85.3 1
 
0.1%
84.7 1
 
0.1%

Cost of Living Index
Real number (ℝ)

High correlation 

Distinct631
Distinct (%)42.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.440401
Minimum17.6
Maximum138.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2025-03-06T02:01:57.528936image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum17.6
5-th percentile27.74
Q136.9
median49.2
Q369.1
95-th percentile86.6
Maximum138.2
Range120.6
Interquartile range (IQR)32.2

Descriptive statistics

Standard deviation20.573712
Coefficient of variation (CV)0.38498424
Kurtosis0.52596254
Mean53.440401
Median Absolute Deviation (MAD)14.5
Skewness0.81270837
Sum79893.4
Variance423.27763
MonotonicityNot monotonic
2025-03-06T02:01:57.644341image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.7 9
 
0.6%
49.2 8
 
0.5%
30.9 8
 
0.5%
41.8 8
 
0.5%
75.9 8
 
0.5%
30.7 8
 
0.5%
35.7 7
 
0.5%
35.1 7
 
0.5%
37.4 7
 
0.5%
31 7
 
0.5%
Other values (621) 1418
94.8%
ValueCountFrequency (%)
17.6 1
0.1%
17.8 1
0.1%
18 1
0.1%
18.5 1
0.1%
18.6 1
0.1%
18.8 1
0.1%
19.3 1
0.1%
19.9 1
0.1%
20.4 1
0.1%
21 2
0.1%
ValueCountFrequency (%)
138.2 1
0.1%
131.7 1
0.1%
131.4 1
0.1%
126 1
0.1%
125.7 2
0.1%
125 1
0.1%
124.5 1
0.1%
123.3 1
0.1%
123.1 1
0.1%
122.7 1
0.1%

Property Price to Income Ratio
Real number (ℝ)

High correlation 

Distinct279
Distinct (%)18.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.876856
Minimum2.6
Maximum202.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2025-03-06T02:01:57.802076image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2.6
5-th percentile4.47
Q18.4
median11.1
Q314.7
95-th percentile26.4
Maximum202.1
Range199.5
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation8.4756372
Coefficient of variation (CV)0.65820702
Kurtosis167.48976
Mean12.876856
Median Absolute Deviation (MAD)3
Skewness8.4473038
Sum19250.9
Variance71.836425
MonotonicityNot monotonic
2025-03-06T02:01:57.918925image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.1 20
 
1.3%
11 19
 
1.3%
12.7 19
 
1.3%
7.5 19
 
1.3%
10.9 18
 
1.2%
9.6 18
 
1.2%
8.6 17
 
1.1%
9.9 17
 
1.1%
10.7 17
 
1.1%
10.6 17
 
1.1%
Other values (269) 1314
87.9%
ValueCountFrequency (%)
2.6 2
 
0.1%
2.7 4
 
0.3%
2.8 7
0.5%
2.9 6
0.4%
3 2
 
0.1%
3.1 6
0.4%
3.2 3
 
0.2%
3.3 10
0.7%
3.4 6
0.4%
3.5 3
 
0.2%
ValueCountFrequency (%)
202.1 1
0.1%
49.4 2
0.1%
47.5 1
0.1%
46.9 2
0.1%
46 1
0.1%
45.7 1
0.1%
45.2 1
0.1%
44.9 1
0.1%
44.4 1
0.1%
43.5 1
0.1%

Traffic Commute Time Index
Real number (ℝ)

High correlation 

Distinct323
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.792508
Minimum11.8
Maximum65.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2025-03-06T02:01:58.054445image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum11.8
5-th percentile25
Q129.9
median34.9
Q340.25
95-th percentile49.1
Maximum65.2
Range53.4
Interquartile range (IQR)10.35

Descriptive statistics

Standard deviation7.9055247
Coefficient of variation (CV)0.22087093
Kurtosis0.73189915
Mean35.792508
Median Absolute Deviation (MAD)5.1
Skewness0.72696623
Sum53509.8
Variance62.49732
MonotonicityNot monotonic
2025-03-06T02:01:58.177065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.1 23
 
1.5%
29.4 20
 
1.3%
35.8 16
 
1.1%
39.4 15
 
1.0%
34.7 15
 
1.0%
33.6 14
 
0.9%
34.8 14
 
0.9%
31.1 14
 
0.9%
28.9 13
 
0.9%
35.6 13
 
0.9%
Other values (313) 1338
89.5%
ValueCountFrequency (%)
11.8 1
0.1%
18.8 1
0.1%
19 1
0.1%
19.5 1
0.1%
19.7 1
0.1%
19.8 1
0.1%
19.9 1
0.1%
20 2
0.1%
20.1 2
0.1%
20.3 2
0.1%
ValueCountFrequency (%)
65.2 1
 
0.1%
64.2 1
 
0.1%
63.4 2
0.1%
63.3 1
 
0.1%
62.8 1
 
0.1%
62.1 1
 
0.1%
62 2
0.1%
61.7 1
 
0.1%
61.1 1
 
0.1%
60.2 4
0.3%

Pollution Index
Real number (ℝ)

High correlation 

Distinct629
Distinct (%)42.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.431237
Minimum11.5
Maximum96.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2025-03-06T02:01:58.301857image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum11.5
5-th percentile19.17
Q133.75
median56.1
Q367.65
95-th percentile84.76
Maximum96.6
Range85.1
Interquartile range (IQR)33.9

Descriptive statistics

Standard deviation20.66028
Coefficient of variation (CV)0.39404525
Kurtosis-1.0593742
Mean52.431237
Median Absolute Deviation (MAD)16.8
Skewness-0.14039278
Sum78384.7
Variance426.84717
MonotonicityNot monotonic
2025-03-06T02:01:58.418422image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67.2 10
 
0.7%
62.9 10
 
0.7%
58.5 9
 
0.6%
75.4 8
 
0.5%
63.1 8
 
0.5%
41 8
 
0.5%
40.4 7
 
0.5%
72.9 7
 
0.5%
72.8 7
 
0.5%
64.5 7
 
0.5%
Other values (619) 1414
94.6%
ValueCountFrequency (%)
11.5 1
 
0.1%
11.6 1
 
0.1%
11.8 2
0.1%
11.9 3
0.2%
12 4
0.3%
12.1 2
0.1%
12.2 1
 
0.1%
12.6 1
 
0.1%
13.4 1
 
0.1%
14.6 1
 
0.1%
ValueCountFrequency (%)
96.6 1
 
0.1%
96 1
 
0.1%
94.6 1
 
0.1%
94.3 1
 
0.1%
94 1
 
0.1%
93.6 1
 
0.1%
92.8 1
 
0.1%
89.5 1
 
0.1%
89.4 5
0.3%
89.3 2
 
0.1%
Distinct414
Distinct (%)27.7%
Missing0
Missing (%)0.0%
Memory size77.1 KiB
2025-03-06T02:01:58.985521image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.7150502
Min length1

Characters and Unicode

Total characters5554
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique200 ?
Unique (%)13.4%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-
ValueCountFrequency (%)
143
 
9.6%
71.2 18
 
1.2%
86.0 17
 
1.1%
89.0 17
 
1.1%
67.8 16
 
1.1%
90.2 16
 
1.1%
59.1 15
 
1.0%
68.4 15
 
1.0%
76.3 15
 
1.0%
57.5 15
 
1.0%
Other values (399) 1208
80.8%
2025-03-06T02:01:59.451623image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 1352
24.3%
8 676
12.2%
7 593
10.7%
9 591
10.6%
6 450
 
8.1%
4 311
 
5.6%
5 304
 
5.5%
2 294
 
5.3%
0 290
 
5.2%
3 290
 
5.2%
Other values (2) 403
 
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5554
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 1352
24.3%
8 676
12.2%
7 593
10.7%
9 591
10.6%
6 450
 
8.1%
4 311
 
5.6%
5 304
 
5.5%
2 294
 
5.3%
0 290
 
5.2%
3 290
 
5.2%
Other values (2) 403
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5554
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 1352
24.3%
8 676
12.2%
7 593
10.7%
9 591
10.6%
6 450
 
8.1%
4 311
 
5.6%
5 304
 
5.5%
2 294
 
5.3%
0 290
 
5.2%
3 290
 
5.2%
Other values (2) 403
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5554
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 1352
24.3%
8 676
12.2%
7 593
10.7%
9 591
10.6%
6 450
 
8.1%
4 311
 
5.6%
5 304
 
5.5%
2 294
 
5.3%
0 290
 
5.2%
3 290
 
5.2%
Other values (2) 403
 
7.3%

Year
Categorical

Distinct20
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size78.9 KiB
2022/2
 
87
2022
 
87
2015
 
86
2024
 
85
2023/2
 
84
Other values (15)
1066 

Length

Max length6
Median length4
Mean length4.9846154
Min length4

Characters and Unicode

Total characters7452
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
2022/2 87
 
5.8%
2022 87
 
5.8%
2015 86
 
5.8%
2024 85
 
5.7%
2023/2 84
 
5.6%
2023 84
 
5.6%
2021 83
 
5.6%
2021/2 83
 
5.6%
2024/2 83
 
5.6%
2020/2 82
 
5.5%
Other values (10) 651
43.5%

Length

2025-03-06T02:01:59.585053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022/2 87
 
5.8%
2022 87
 
5.8%
2015 86
 
5.8%
2024 85
 
5.7%
2023/2 84
 
5.6%
2023 84
 
5.6%
2021 83
 
5.6%
2021/2 83
 
5.6%
2024/2 83
 
5.6%
2020/2 82
 
5.5%
Other values (10) 651
43.5%

Most occurring characters

ValueCountFrequency (%)
2 3243
43.5%
0 1657
22.2%
1 823
 
11.0%
/ 736
 
9.9%
4 168
 
2.3%
3 168
 
2.3%
9 148
 
2.0%
5 143
 
1.9%
8 126
 
1.7%
7 123
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7452
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 3243
43.5%
0 1657
22.2%
1 823
 
11.0%
/ 736
 
9.9%
4 168
 
2.3%
3 168
 
2.3%
9 148
 
2.0%
5 143
 
1.9%
8 126
 
1.7%
7 123
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7452
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 3243
43.5%
0 1657
22.2%
1 823
 
11.0%
/ 736
 
9.9%
4 168
 
2.3%
3 168
 
2.3%
9 148
 
2.0%
5 143
 
1.9%
8 126
 
1.7%
7 123
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7452
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 3243
43.5%
0 1657
22.2%
1 823
 
11.0%
/ 736
 
9.9%
4 168
 
2.3%
3 168
 
2.3%
9 148
 
2.0%
5 143
 
1.9%
8 126
 
1.7%
7 123
 
1.7%

Interactions

2025-03-06T02:01:54.145739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2025-03-06T02:01:54.312344image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2025-03-06T02:01:50.123170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2025-03-06T02:01:52.708436image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-06T02:01:53.520960image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-06T02:01:54.395499image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-06T02:01:47.091289image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-06T02:01:48.029900image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-06T02:01:49.389510image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2025-03-06T02:01:52.787933image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-06T02:01:53.604393image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-06T02:01:54.694822image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-06T02:01:47.199811image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-06T02:01:48.149471image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-06T02:01:49.506705image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-06T02:01:50.301434image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-06T02:01:51.105405image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-06T02:01:52.071824image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-06T02:01:52.893246image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-06T02:01:53.695981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-06T02:01:54.778774image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-06T02:01:47.295568image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-06T02:01:48.269149image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-06T02:01:49.599287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-06T02:01:50.391790image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-06T02:01:51.197032image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-06T02:01:52.163368image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-06T02:01:52.979710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-06T02:01:53.787887image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-06T02:01:54.862084image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-06T02:01:47.381784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-06T02:01:48.393993image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-06T02:01:49.680727image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-06T02:01:50.475599image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-06T02:01:51.288665image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-06T02:01:52.246556image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-06T02:01:53.071280image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2025-03-06T02:01:49.866465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-06T02:01:50.663847image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-06T02:01:51.621865image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-06T02:01:52.438236image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-06T02:01:53.262400image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-06T02:01:54.054206image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2025-03-06T02:01:59.676903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Cost of Living IndexHealth Care IndexPollution IndexProperty Price to Income RatioPurchasing Power IndexQuality of Life IndexRankSafety IndexTraffic Commute Time IndexYear
Cost of Living Index1.0000.567-0.648-0.4210.7430.686-0.7320.420-0.3680.063
Health Care Index0.5671.000-0.484-0.2080.5560.561-0.5800.350-0.1610.000
Pollution Index-0.648-0.4841.0000.479-0.630-0.8500.859-0.3960.5770.000
Property Price to Income Ratio-0.421-0.2080.4791.000-0.635-0.6560.669-0.1640.4160.000
Purchasing Power Index0.7430.556-0.630-0.6351.0000.821-0.8440.460-0.3950.146
Quality of Life Index0.6860.561-0.850-0.6560.8211.000-0.9590.535-0.6200.180
Rank-0.732-0.5800.8590.669-0.844-0.9591.000-0.5350.6200.000
Safety Index0.4200.350-0.396-0.1640.4600.535-0.5351.000-0.4800.000
Traffic Commute Time Index-0.368-0.1610.5770.416-0.395-0.6200.620-0.4801.0000.000
Year0.0630.0000.0000.0000.1460.1800.0000.0000.0001.000

Missing values

2025-03-06T02:01:55.153276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-06T02:01:55.312112image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

RankCountryQuality of Life IndexPurchasing Power IndexSafety IndexHealth Care IndexCost of Living IndexProperty Price to Income RatioTraffic Commute Time IndexPollution IndexClimate IndexYear
01Switzerland222.9146.573.266.3126.07.325.624.1-2015
12Germany195.9111.871.575.676.36.631.128.3-2015
23Sweden193.9110.957.976.382.99.226.915.1-2015
34United States192.5126.150.067.876.52.636.031.4-2015
45Finland190.2101.270.569.489.77.033.214.9-2015
56Denmark190.2105.774.379.3100.65.027.831.8-2015
67Austria182.6104.673.878.476.99.625.934.0-2015
78Australia180.8110.457.871.999.37.136.221.6-2015
89Canada177.6105.162.569.681.85.538.927.3-2015
910New Zealand175.591.060.272.693.75.828.117.6-2015
RankCountryQuality of Life IndexPurchasing Power IndexSafety IndexHealth Care IndexCost of Living IndexProperty Price to Income RatioTraffic Commute Time IndexPollution IndexClimate IndexYear
148574Thailand98.440.862.277.234.126.138.875.758.42024/2
148675Vietnam97.240.558.261.428.722.629.684.271.22024/2
148776Philippines90.833.357.067.131.025.842.072.661.22024/2
148877Lebanon86.525.453.463.741.824.338.789.494.72024/2
148978Egypt86.020.052.747.521.015.548.182.788.52024/2
149079Peru84.634.832.756.330.018.348.982.597.72024/2
149180Iran80.426.049.752.526.423.446.475.371.02024/2
149281Sri Lanka75.217.557.971.034.639.154.658.559.12024/2
149382Bangladesh72.633.138.442.222.512.357.685.671.32024/2
149483Nigeria42.711.033.348.231.423.964.288.260.82024/2